Effects of Endurance Training Intensity on Pulmonary Diffusing Capacity at Rest and after Maximal Aerobic Exercise in Young Athletes
Bibliographic record
Abstract
This study compared the effects of varying aerobic training programs on pulmonary diffusing capacity (TLCO), pulmonary diffusing capacity for nitric oxide (TLNO), lung capillary blood volume (Vc) and alveolar–capillary membrane diffusing capacity (DM) of gases at rest and just after maximal exercise in young athletes. Sixteen healthy young runners (16–18 years) were randomly assigned to an intense endurance training program (IET, n = 8) or to a moderate endurance training program (MET, n = 8). The training volume was similar in IET and MET but with different work intensities, and each lasted for 8 weeks. Participants performed a maximal graded cycle bicycle ergometer test to measure maximal oxygen consumption (VO2max) and maximal aerobic power (MAP) before and after the training programs. Moreover, TLCO, TLNO and Vc were measured during a single breath maneuver. After eight weeks of training, all pulmonary parameters with the exception of alveolar volume (VA) and inspiratory volume (VI) (0.104 < p < 0889; 0.001 < ES < 0.091), measured at rest and at the end of maximal exercise, showed significant group × time interactions (p < 0.05, 0.2 < ES < 4.0). Post hoc analyses revealed significant pre-to-post decreases for maximal heart rates (p < 0.0001, ES = 3.1) and improvements for VO2max (p = 0.006, ES = 2.22) in the IET group. Moreover, post hoc analyses revealed significant pre-to-post improvements in the IET for DM, TLNO, TLCO and Vc (0.001 < p < 0.0022; 2.68 < ES < 6.45). In addition, there were increases in Vc at rest, VO2max, TLNO and DM in the IET but not in the MET participants after eight weeks of training with varying exercise intensities. Our findings suggest that the intensity of training may represent the most important factor in increasing pulmonary vascular function in young athletes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".